import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
import math
import triton
import triton.language as tl
from models.custom_autotune import *


def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
    if type(module) in layers:
        return {name: module}
    res = {}
    for name1, child in module.named_children():
        res.update(find_layers(
            child, layers=layers, name=name + '.' + name1 if name != '' else name1
        ))
    return res


# code based https://github.com/fpgaminer/GPTQ-triton
@autotune(
    configs=[
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        # These provided a benefit on a 3090
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
    ],
    key=['M', 'N'],
    nearest_power_of_two=True,
)
@triton.jit
def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                      scales_ptr, zeros_ptr, g_ptr,
                      M, N, K, bits, maxq,
                      stride_am, stride_ak,
                      stride_bk, stride_bn,
                      stride_cm, stride_cn,
                      stride_scales, stride_zeros,
                      BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                      GROUP_SIZE_M: tl.constexpr):
    """
    Compute the matrix multiplication C = A x B.
    A is of shape (M, K) float16
    B is of shape (K//8, N) int32
    C is of shape (M, N) float16
    scales is of shape (G, N) float16
    zeros is of shape (G, N) float16
    g_ptr is of shape (K) int32
    """
    infearure_per_bits = 32 // bits

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
    num_pid_in_group = GROUP_SIZE_M * num_pid_n
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + (pid % group_size_m)
    pid_n = (pid % num_pid_in_group) // group_size_m

    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    offs_k = tl.arange(0, BLOCK_SIZE_K)
    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
    a_mask = (offs_am[:, None] < M)
    # b_ptrs is set up such that it repeats elements along the K axis 8 times
    b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None,
                                                                            :] * stride_bn)  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
    g_ptrs = g_ptr + offs_k
    # shifter is used to extract the N bits of each element in the 32-bit word from B
    scales_ptrs = scales_ptr + offs_bn[None, :]
    zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)

    shifter = (offs_k % infearure_per_bits) * bits
    zeros_shifter = (offs_bn % infearure_per_bits) * bits
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)

    for k in range(0, num_pid_k):
        g_idx = tl.load(g_ptrs)

        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
        scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)

        zeros = (zeros >> zeros_shifter[None, :]) & maxq
        zeros = (zeros + 1)

        a = tl.load(a_ptrs, mask=a_mask, other=0.)  # (BLOCK_SIZE_M, BLOCK_SIZE_K)
        b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

        # Now we need to unpack b (which is N-bit values) into 32-bit values
        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
        b = (b - zeros) * scales  # Scale and shift

        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_K
        b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
        g_ptrs += BLOCK_SIZE_K

    c = accumulator.to(tl.float16)
    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
    c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
    tl.store(c_ptrs, accumulator, mask=c_mask)


# code based https://github.com/fpgaminer/GPTQ-triton
@autotune(
    configs=[
        triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
        # These provided a benefit on a 3090
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
                      num_warps=4),
        triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8},
                      num_stages=4, num_warps=4),
    ],
    key=['M', 'K'],
    nearest_power_of_two=True,
)
@triton.jit
def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
                            scales_ptr, zeros_ptr, g_ptr,
                            M, N, K, bits, maxq,
                            stride_am, stride_ak,
                            stride_bk, stride_bn,
                            stride_cm, stride_cn,
                            stride_scales, stride_zeros,
                            BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
                            GROUP_SIZE_M: tl.constexpr):
    """
    Compute the matrix multiplication C = A x B.
    A is of shape (M, N) float16
    B is of shape (K//8, N) int32
    C is of shape (M, K) float16
    scales is of shape (G, N) float16
    zeros is of shape (G, N) float16
    g_ptr is of shape (K) int32
    """
    infearure_per_bits = 32 // bits

    pid = tl.program_id(axis=0)
    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
    num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
    num_pid_in_group = GROUP_SIZE_M * num_pid_k
    group_id = pid // num_pid_in_group
    first_pid_m = group_id * GROUP_SIZE_M
    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
    pid_m = first_pid_m + (pid % group_size_m)
    pid_k = (pid % num_pid_in_group) // group_size_m

    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
    offs_n = tl.arange(0, BLOCK_SIZE_N)
    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak)  # (BLOCK_SIZE_M, BLOCK_SIZE_N)
    a_mask = (offs_am[:, None] < M)
    # b_ptrs is set up such that it repeats elements along the K axis 8 times
    b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None,
                                                                             :] * stride_bn)  # (BLOCK_SIZE_K, BLOCK_SIZE_N)
    g_ptrs = g_ptr + offs_bk
    g_idx = tl.load(g_ptrs)

    # shifter is used to extract the N bits of each element in the 32-bit word from B
    scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
    zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros

    shifter = (offs_bk % infearure_per_bits) * bits
    zeros_shifter = (offs_n % infearure_per_bits) * bits
    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)

    for k in range(0, num_pid_n):
        # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
        scales = tl.load(scales_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
        zeros = tl.load(zeros_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N,)

        zeros = (zeros >> zeros_shifter[None, :]) & maxq
        zeros = (zeros + 1)

        a = tl.load(a_ptrs, mask=a_mask, other=0.)  # (BLOCK_SIZE_M, BLOCK_SIZE_N)
        b = tl.load(b_ptrs)  # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated

        # Now we need to unpack b (which is N-bit values) into 32-bit values
        b = (b >> shifter[:, None]) & maxq  # Extract the N-bit values
        b = (b - zeros) * scales  # Scale and shift
        b = tl.trans(b)

        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_N
        b_ptrs += BLOCK_SIZE_N
        scales_ptrs += BLOCK_SIZE_N
        zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)

    c = accumulator.to(tl.float16)
    c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
    c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
    tl.store(c_ptrs, accumulator, mask=c_mask)


def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
    output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
    grid = lambda META: (
    triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
    matmul_248_kernel[grid](input, qweight, output,
                            scales, qzeros, g_idx,
                            input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
                            input.stride(0), input.stride(1),
                            qweight.stride(0), qweight.stride(1),
                            output.stride(0), output.stride(1),
                            scales.stride(0), qzeros.stride(0))
    return output


def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
    output_dim = (qweight.shape[0] * 32) // bits
    output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16)
    grid = lambda META: (
    triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),)
    transpose_matmul_248_kernel[grid](input, qweight, output,
                                      scales, qzeros, g_idx,
                                      input.shape[0], qweight.shape[1], output_dim, bits, maxq,
                                      input.stride(0), input.stride(1),
                                      qweight.stride(0), qweight.stride(1),
                                      output.stride(0), output.stride(1),
                                      scales.stride(0), qzeros.stride(0))
    return output


class QuantLinearFunction(torch.autograd.Function):
    @staticmethod
    @custom_fwd(cast_inputs=torch.float16)
    def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
        output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
        ctx.save_for_backward(qweight, scales, qzeros, g_idx)
        ctx.bits, ctx.maxq = bits, maxq
        return output

    @staticmethod
    @custom_bwd
    def backward(ctx, grad_output):
        qweight, scales, qzeros, g_idx = ctx.saved_tensors
        bits, maxq = ctx.bits, ctx.maxq
        grad_input = None

        if ctx.needs_input_grad[0]:
            grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
        return grad_input, None, None, None, None, None, None

class QuantLinear(nn.Module):
    def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
        super().__init__()
        if bits not in [2, 4, 8]:
            raise NotImplementedError("Only 2,4,8 bits are supported.")
        self.infeatures = infeatures
        self.outfeatures = outfeatures
        self.bits = bits
        self.maxq = 2 ** self.bits - 1
        self.groupsize = groupsize if groupsize != -1 else infeatures

        self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
        self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
        self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
        self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
        if bias:
            self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
        else:
            self.bias = None

    def pack(self, linear, scales, zeros, g_idx=None):
        self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx

        scales = scales.t().contiguous()
        zeros = zeros.t().contiguous()
        scale_zeros = zeros * scales
        self.scales = scales.clone().half()
        if linear.bias is not None:
            self.bias = linear.bias.clone().half()

        intweight = []
        for idx in range(self.infeatures):
            intweight.append(torch.round(
                (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(
                torch.int)[:, None])
        intweight = torch.cat(intweight, dim=1)
        intweight = intweight.t().contiguous()
        intweight = intweight.numpy().astype(np.uint32)
        qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
        i = 0
        row = 0
        while row < qweight.shape[0]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qweight[row] |= intweight[j] << (self.bits * (j - i))
                i += 32 // self.bits
                row += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qweight = qweight.astype(np.int32)
        self.qweight = torch.from_numpy(qweight)

        zeros -= 1
        zeros = zeros.numpy().astype(np.uint32)
        qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
        i = 0
        col = 0
        while col < qzeros.shape[1]:
            if self.bits in [2, 4, 8]:
                for j in range(i, i + (32 // self.bits)):
                    qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
                i += 32 // self.bits
                col += 1
            else:
                raise NotImplementedError("Only 2,4,8 bits are supported.")

        qzeros = qzeros.astype(np.int32)
        self.qzeros = torch.from_numpy(qzeros)

    def forward(self, x):
        out_shape = x.shape[:-1] + (self.outfeatures,)
        out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales,
                                        self.qzeros, self.g_idx, self.bits, self.maxq)
        out = out + self.bias if self.bias is not None else out
        return out.reshape(out_shape)

def make_quant(module, names, bits, groupsize, name=''):
    if isinstance(module, QuantLinear):
        return
    for attr in dir(module):
        tmp = getattr(module, attr)
        name1 = name + '.' + attr if name != '' else attr
        if name1 in names:
            delattr(module, attr)
            setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
    for name1, child in module.named_children():
        make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)


def quantize_with_gptq(model, wbits, groupsize):
    model = model.eval()
    layers = find_layers(model)
    for name in ['lm_head']:
        if name in layers:
            del layers[name]
    make_quant(model, layers, wbits, groupsize)
    # model.load_state_dict(torch.load(checkpoint))
    return model
